Downside Risk Spillover among Global Real Estate Securities Markets

Zhou, Jian, Journal of Real Estate Portfolio Management

Executive Summary.

This paper examines the spillover of extreme downside risks among global real estate securities markets. The newly developed conditional autoregressive value at risk (CAViaR) model (Engle and Manganelli, 2004) is used to estimate Value-at-Risk (VaR). The VaR estimates are used to study two forms of spillover: simultaneous spillover and conditional spillover. The findings show that a majority of market pairs under consideration display significant intensity for both forms of spillover. This suggests strong comovement of extreme downside risks among the markets, as well as strong risk flow from one to another. Further analyses using regression show that intensities of both simultaneous and conditional spillovers are significantly higher between markets from the same region than between markets from different regions. Moreover, sample correlations of returns and the relative size of markets are found to have no significant impacts on spillover intensities.

(ProQuest: ... denotes formulae omitted.)

In recent years global financial markets have been experiencing a lot of turmoil. During times of financial turmoil, large swings of asset prices tend to spread across countries. It has therefore become increasingly important to understand how financial risks are transmitted from one market to another. In this regard, the probability of a large downside market movement is always of great concern to financial players (e.g., Bollerslev, 2001). If they occur, extreme market movements involve a huge amount of capital changing hands among market participants, inevitably leading some to bankruptcies. This point has been perfectly exemplified in the recent financial crisis.

Against this backdrop, it is of great value to investigate how extreme downside risks spill over among international financial markets. In the general finance literature, efforts have been made to examine this issue. For instance, Bae, Karolyi, and Stulz (2003) attempted to capture the co-incidence of extreme return shocks across a number of emerging stock markets using fundamental economic variables including interest rates, exchange rate changes, and conditional volatility. Asgharian and Bengtsson (2006) and Asgharian and Nossman (2011) analyzed extreme risk spillover in the form of jumps in returns and volatility for a selection of equity markets. Hong, Liu, and Wang (2009) used the Granger-causality technique to detect extreme downside risk spillover among exchange rate markets. Finally, Liu (2011) employed a probit model to study extreme downside risk spillovers from large stock markets of the United States and Japan to some relatively small markets in AsianPacific region. In contrast with the general finance literature, real estate literature has not yet attended to the spillover of extreme downside risks. The reason is that only a very few papers (e.g., Liow, 2008; Lu, Wu, and Ho, 2009; Zhou and Anderson, 2010; Zhou, 2012) have studied extreme risks for real estate markets, let alone analyze risk spillover. This is clearly out of phase with the growing importance of real estate securities as an asset class. To help close the knowledge gap, this paper attempts to make two contributions: one is to add to the literature by taking advantage of a newly developed method to estimate extreme downside risks for real estate securities markets, and two, more importantly, is to fill the void by examining how downside risks are transmitted across markets.

First, a measure is needed for extreme downside risk. Value-at-risk (VaR) estimates how much a portfolio can lose within a given time period with a pre-specified probability. As a downside risk measure, VaR concentrates on low probability events that occur in the left tail of a financial distribution, which is inherently related to extreme downward movements (e.g., Embrechts, Kluppelberg, and Mikosch, 1997). Originally proposed by J.R Morgan in 1994, VaR has since been widely embraced by risk managers as an important tool to assess risks and by regulatory bodies to set margin requirements (e. …

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